Hyperspectral and Thermal Sensors to Improve the Prediction of Agronomic Variables in Different Winter Wheat Genotypes

Remote sensing offers great potential to monitor crop performance, which could help to improve water and nitrogen (N) management. The aim of this study is to assess the nutritional and water status of two wheat (Triticum aestivum L.) genotypes (Cellule and Nogal) to determine their performance by means of vegetation indices, plant traits retrieved by a radiative transfer model and thermal data. To this end, two field experiments were conducted in central Spain during 2018–2021. The results showed that the best differentiation between genotype performance was achieved by predicted chlorophyll (Chl) and leaf area index retrieved through the PROSAIL model and the canopy Chl content index (CCCI), showing that the Cellule genotype had a stronger response than Nogal to N application. Similarly, the water deficit index and canopy–air temperature difference showed that Cellule suffered lower water stress than Nogal.